Principal Engineer, AI/ML Systems (Context Services)
Not sure if you're a good fit?
Upload your resume and TixelJobs AI will compare it against Principal Engineer, AI/ML Systems (Context Services) at Cognite. Get a match score, missing keywords, and improvement tips before you apply.
Free preview · Your resume stays private
About the Role
What Cognite is: Relentless to achieve
Cognite operates at the forefront of industrial digitalization, building AI, and data solutions that solve the world’s hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements.
We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you’ll feel right at home here.
Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.
About the Opportunity
About the Team:
Our Context Services team turns unstructured industrial data into structured, actionable intelligence that industrial operators rely on for efficiency and safety. We work at the intersection of complex data problems and production-grade systems, serving these solutions reliably to thousands of users across demanding industrial environments.
About the Role:
We are looking for a Principal Engineer to own the technical architecture for Context Services. You will set the system-level direction that bridges our AI Engineers and the central Platform Engineering team, converting complex analytical solutions into highly reliable, production-grade software services at scale. This is the senior-most technical voice in the group, responsible for architecture decisions that other engineers build against.
- A seasoned builder who sets the engineering bar for the team, you design fault-tolerant, scalable architectures from scratch and push the team toward production-grade best practices.
- Someone who balances technical rigor with business reality, you anticipate system bottlenecks, make the hard architectural trade-offs, and translate business goals into executable technical strategy.
- A trusted technical advisor, you take ownership of large, ambiguous projects from conception through deployment, and mentor engineers along the way.
The Impact you bring to Cognite
Key Responsibilities:
- Architecture & Scale: Define the system architecture for Context Services end-to-end; design low-latency APIs/services supporting thousands of concurrent users with sub-second response times.
- Cross-Functional Leadership: Partner with product, architecture, and engineering leadership to translate ambiguous industrial data problems into scalable, sequenced technical strategy.
- AI & LLM Orchestration: Architect the frameworks for model serving, vector databases, and agentic workflows, including reliable state management, prompt tracking, and orchestration-layer design.
- Platform Integration: Serve as the primary technical liaison with the central Platform team, defining how Context Services leverages and extends core infrastructure.
- Technical Standards & Mentorship: Set engineering standards and review practices for the team; mentor Senior and mid-level engineers; unblock the hardest cross-cutting technical problems.
- Innovation: Drive technical innovation through research, prototyping, and open-source contributions where relevant.
Required Skills and Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Science, or a related field (PhD often preferred at this level).
- 10+ years of industry experience in machine learning and software development.
- Strong programming skills in Python, C++, or Java, with deep proficiency building high-concurrency, asynchronous applications and REST/gRPC APIs at scale.
- Expertise in frameworks like PyTorch, TensorFlow, Keras, or Scikit-learn.
- Solid understanding of containerization (Docker) and deployment environments (Kubernetes, cloud platforms); experience architecting for fault tolerance and reliability.
- Familiarity with MLOps best practices and cloud platforms such as AWS, Azure, or GCP.
- Solid understanding of data structures, algorithms, and software architecture.
Preferred Qualifications:
- Deep expertise designing complex AI systems from the ground up
- Experience architecting modern lakehouses (e.g., Delta Lake, Apache Iceberg) to process and manage massive, complex datasets specific to manufacturing, supply chain, or OT environments.
- Proven ability to optimize large language models for maximum throughput and low latency, with experience deploying AI into highly secure, on-premises, or edge environments.
- Strong familiarity with the broader AI lifecycle model i.e registries, vector/graph databases, LLM evaluation frameworks, agentic orchestration.
What Sets This Role Apart:
- The ability to lead large, complex projects from design through deployment, and to define the architecture that other engineers execute against.
- Experience with high-scale ML environments, distributed computing, and optimizing model inference latency.
- Proven ability to translate complex ML/systems concepts into business insights for stakeholders and leadership.
- Impact 2025
- Cognite's Industrial AI: Moonshot
- We’re globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.
Ready to apply?
This job is active. Apply now to get in early.